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Description/Abstract

Forest biophysical properties are typically estimatedand mapped from remotely sensed data through theapplication of a vegetation index. This generallydoes not make full use of the information contentof the remotely sensed data, using only the dataacquired in a limited number of spectral channels,and may provide a relatively crude spatial representationof the biophysical variable of interest. Usingimagery acquired by the NOAA AVHRR, it is shownthat a standard neural network may use all thespectral channels available in a remotely senseddata set to derive more accurate estimates of thebiophysical properties of tropical forests in Ghanathan a series of vegetation indices. Additionally, thespatial representation derived can be refined byfusion with finer spatial resolution imagery, achievedwith the application of a further neural network.